Quality Prediction

Adam Bittlingmayer, Boris Zubarev, Artur Aleksanyan


Abstract
A growing share of machine translations are approved - untouched - by human translators in post-editing workflows. But they still cost time and money. Now companies are getting human post-editing quality faster and cheaper, by automatically approving the good machine translations - at human accuracy. The approach has evolved, from research papers on machine translation quality estimation, to adoption inside companies like Amazon, Facebook, Microsoft and VMWare, to self-serve cloud APIs like ModelFront. We’ll walk through the motivations, use cases, prerequisites, adopters, providers, integration and ROI.
Anthology ID:
2022.amta-upg.12
Volume:
Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track)
Month:
September
Year:
2022
Address:
Orlando, USA
Editors:
Janice Campbell, Stephen Larocca, Jay Marciano, Konstantin Savenkov, Alex Yanishevsky
Venue:
AMTA
SIG:
Publisher:
Association for Machine Translation in the Americas
Note:
Pages:
159–180
Language:
URL:
https://aclanthology.org/2022.amta-upg.12
DOI:
Bibkey:
Cite (ACL):
Adam Bittlingmayer, Boris Zubarev, and Artur Aleksanyan. 2022. Quality Prediction. In Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track), pages 159–180, Orlando, USA. Association for Machine Translation in the Americas.
Cite (Informal):
Quality Prediction (Bittlingmayer et al., AMTA 2022)
Copy Citation:
Presentation:
 2022.amta-upg.12.Presentation.pdf